通过基于质谱的蛋白质组学鉴定高通量蛋白质的蛋白质概率模型。,Journal of Proteome Research

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通过基于质谱的蛋白质组学鉴定高通量蛋白质的蛋白质概率模型。
Journal of Proteome Research ( IF 3.8 ) Pub Date : 2020-02-25 , DOI: 10.1021/acs.jproteome.9b00819
Gorka Prieto ₁ , Jesús Vázquez ₂

Affiliation

gun弹枪蛋白质组学是高通量蛋白质鉴定的首选方法。但是，强大的统计方法对于自动执行此任务同时最小化错误标识的数量至关重要。目标诱骗法是估计个体识别的错误发现率（FDR）并将其保持在阈值（通常为1％）以下的标准方法。但是，许多工作表明，蛋白质水平的FDR可能比肽水平的FDR大得多。迫切需要开发一种合适的评分模型，以使用高通量shot弹枪蛋白质组学从其肽中鉴定蛋白质。在这项研究中，我们提出了一种新颖的蛋白质水平评分算法，该算法使用已鉴定肽段的分数，并保留了预期的真实蛋白质概率的所有特性。选择一种方法来计算蛋白质水平的FDR。这些算法可以一起用作适合大规模蛋白质组学的鲁棒识别工作流程，并且我们证明该工作流程的识别性能在几个示例中以及使用不同的搜索引擎时要优于其他广泛使用的方法。我们的蛋白质概率模型为科学界提供了一种易于集成到蛋白质识别工作流程中的算法，可以自动分析shot弹枪蛋白质组学数据。

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Protein Probability Model for High-Throughput Protein Identification by Mass Spectrometry-Based Proteomics.

Shotgun proteomics is the method of choice for high-throughput protein identification; however, robust statistical methods are essential to automatize this task while minimizing the number of false identifications. The standard method for estimating the false discovery rate (FDR) of individual identifications and keeping it below a threshold (typically 1%) is the target-decoy approach. However, numerous works have shown that FDR at the protein level may become much larger than FDR at the peptide level. The development of an appropriate scoring model to identify proteins from their peptides using high-throughput shotgun proteomics is highly needed. In this study, we present a novel protein-level scoring algorithm that uses the scores of the identified peptides and maintains all of the properties expected for a true protein probability. We also present a refinement of the picked method to calculate FDR at the protein level. These algorithms can be used together as a robust identification workflow suitable for large-scale proteomics, and we show that the identification performance of this workflow is superior to that of other widely used methods in several samples and using different search engines. Our protein probability model offers the scientific community an algorithm that is easy to integrate into protein identification workflows for the automated analysis of shotgun proteomics data.

更新日期：2020-02-25

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